CN114926977A - Multitask allocation method suitable for collaborative automatic driving - Google Patents
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Abstract
The invention discloses a multitask allocation method suitable for vehicle fog calculation in collaborative automatic driving, and belongs to the field of collaborative automatic driving. The invention combines vehicle fog calculation with an MIMO system and adopts analog beamforming. At the transmitter, the task data signals generated by each client vehicle are converted into parallel signal vectors through serial-parallel conversion, and then transmission signal vectors are generated through a hybrid pre-coding matrix. At the receiver, the transmission signal vector is transformed and decoded by a channel matrix to obtain a final signal vector. And calculating the channel capacity of the antenna array, and modeling the optimization problem of the energy consumption and the transmission rate of the multiple vehicles as a joint optimization problem function. The function is transformed into an iterative solution model which can be approximated by adding constraints and applying convex set programming. And the management node calculates a task allocation method according to the model and issues and implements the task allocation method. The method is suitable for the field of automatic driving, realizes efficient parallel unloading tasks when vehicles with multiple antennas cooperate with automatic driving, improves data transmission rate and reduces energy consumption.
Description
Technical Field
The invention relates to a multitask allocation method suitable for vehicle fog calculation in collaborative automatic driving, and belongs to the field of collaborative automatic driving.
Background
In recent years, in the field of automobile cooperative automatic driving, large companies around the world have been increasing the level of cooperative automatic driving, and attempts have been made to bring cooperative automatic driving automobiles of the L2, L3, L4 level into the consumer market. For coordinated autopilot, if it is said that the algorithmic capability determines an upper limit of the coordinated autopilot capability, then the vehicle gauge chip determines a lower limit. The lowest computing power standard of L5 level cooperative automatic driving is 500+ TOPS, while the current most top vehicle gauge chip Yingwei Orin x computing power only reaches 254TOPS, the domestic most top chip computing power only reaches 72TOPS, and supply and demand are not satisfied. So for most vehicles, the force is far from satisfying the demand. For example, in the automatic parking scheme of the passenger car in the fourth quarter of 2019, 92.8% of vehicles select the ultrasonic scheme, and 7.2% of vehicles select the ultrasonic + camera scheme. Although the latter effect is obviously better, the image processing of 288 frames/second can not be supported by the chip computing power, and the image processing of the full frequency of 36 frames/second of the 8 cameras of the whole vehicle has to be abandoned. Meanwhile, the energy consumption is greatly increased by cooperating with a high-performance chip and a redundant sensor required by automatic driving. Cloud computing is a main platform for analyzing and processing big data of the current Internet of vehicles. However, cloud computing has many defects, and data transmission far away from the vehicle-mounted terminal not only occupies a large part of network bandwidth, but also increases network transmission delay, causes emergency processing delay, and causes excessive congestion of a large number of vehicles. Vehicle Fog Computing (VFC) concentrates communication and computing resources at the edge of the network, becoming a popular computing paradigm in vehicle-mounted scenarios. The application of the vehicle fog calculation in the cooperative automatic driving can not only provide calculation power, but also reduce delay and energy consumption, and effectively solve the problems. The key of the vehicle-mounted fog calculation lies in unloading of tasks, and different task unloading technologies and methods determine energy consumption and delay of application implementation.
The cooperative automatic driving system comprises three parts of perception, decision planning and driving control, perception data obtained by a vehicle sensor is compressed by a processing algorithm, and then can be unloaded and sent to a service vehicle through a vehicle fog calculation task, namely a large vehicle carrying a large amount of calculation resources is subjected to decision planning and driving control. The cooperative automatic driving function is continuously developed, and the functions generate more and more application tasks from the initial automatic emergency braking to the present automatic AVP (automated Valet parking) automatic Valet parking and to the future urban navigation. In a collaborative automatic driving scene, the fate of application task processing is low time delay and low energy consumption. Therefore, the task allocation method with low time delay, low energy consumption and parallel becomes the key point of research. Alahmadi et al uses a mixed integer linear programming model to determine the task allocation patterns, which reduces the total system energy consumption by reducing its average processing workload and network traffic, but this method does not take into account service delays. Moreover, these theoretical studies and platform simulations only stop at the network level of task offloading, and the configuration of the vehicle itself has not been considered. As the antenna manufacturing technology becomes more mature, the cost of the antenna becomes lower, and it is possible to install a plurality of wireless antennas on one vehicle. The multi-antenna can effectively utilize channels, remarkably improve the data transmission rate and reduce the energy consumption. However, the transmission of different antennas on channels within the same frequency band causes significant interference to each other. How to effectively distribute these different tasks in the vehicle fog computing network is also a problem to reduce communication interference while enabling more tasks to be transmitted in parallel.
Disclosure of Invention
In order to overcome the defects of high energy consumption, high calculation force requirement and low data transmission rate in the prior art of cooperative automatic driving, the invention mainly aims to provide a multitask allocation method suitable for cooperative automatic driving, and the efficient parallel unloading task of a vehicle with multiple antennas in cooperative automatic driving is realized through vehicle fog calculation, so that the data transmission rate is improved, and the energy consumption is reduced.
The purpose of the invention is realized by the following technical scheme:
the invention discloses a multi-task allocation method suitable for vehicle fog calculation in cooperative automatic driving, which combines vehicle fog calculation with an MIMO system and adopts an analog beam forming technology. At the transmitter, the task data signals generated by each client vehicle are converted into parallel signal vectors through serial-parallel conversion, and then transmission signal vectors are generated through a hybrid pre-coding matrix. At the receiver, the transmission signal vector is transformed and decoded by a channel matrix to obtain a final signal vector. And calculating the channel capacity of the antenna array, and modeling the optimization problem of the energy consumption and the transmission rate of the multiple vehicles as a joint optimization problem function. The function is transformed into an iterative solution model which can be approximated by adding constraints and applying convex set programming. In an application scene, a client vehicle sends a request, and a management node calculates a task allocation method according to a model and issues and implements the task allocation method. The efficient parallel unloading task is realized when the vehicle with multiple antennas cooperates with automatic driving, so that the data transmission rate is improved, and the energy consumption is reduced.
The invention discloses a multi-task allocation method suitable for collaborative automatic driving, which comprises the following steps:
step 1: modeling a non-convex and non-concave optimization problem of the task allocation scheme into a solvable approximate optimization problem, and realizing effective reduction of energy consumption and delay by solving the approximate optimal task allocation scheme;
step 1.1, converting a plurality of task data streams generated by a single-customer vehicle into parallel signal vectors s through serial-to-parallel conversion i From
That is, parallel signal vectors pass through digital pre-coding matrix and analog pre-coding matrix to obtain transmission vector x i Where I ═ { I } denotes a client vehicle, J ═ { J } denotes a set of server vehicles, K i K denotes a task generated by the customer vehicle i, a ij 0,1 represents the geographic association of the client vehicle i and the service vehicle j, with a value of 1 if j is within communication range of i,
indicating whether task k will be assigned to service vehicle j, is assigned when the value is 1, is not assigned when the value is 0, G i A digital precoding matrix for vehicle i, B i For the simulated precoding matrix, s, of vehicle i i ＝[s1,s2,···,s|K i |]Representing parallel signal vectors, s, generated by vehicle i k Signal representing task k, g k A digital precoding vector, x, representing task k i A transmission signal vector representing vehicle i;
step 1.2: transmission vector x i The signal vector received by the service vehicle can be obtained through channel matrix transformation
Decoding to obtain signal vector
The method is characterized by comprising three parts, namely an expected signal of a service vehicle, other interference signals and noise interference. According to
The antenna array channel capacity for task k between customer vehicle i and service vehicle j can be calculated as
Wherein sigma 2 Is the variance of white Gaussian noise and is a constant; k ' of strip refers to removing the set K ' of K after the ith task ' i The task of (1). Setting the use of a uniform digital precoding method for each task generated, i.e. g k G, let h ij ＝|f j ·H ij ·g·B i L2, then simplify
Is composed of
Indicating the transmission power allocated to task k, f j Is the analog beamforming vector, H, used by the service vehicle j ij Representing the channel matrix between client i and server j,
an antenna array channel capacity representing a task k between a customer vehicle i and a service vehicle j;
step 1.3: setting a sensitivity variable alpha representing the data rate, the optimization problem function P1 is
Wherein alpha is [0,1 ]]The weight indicating the degree of sensitivity to the rate, it is clear that the larger the α, the larger the
The greater the influence of (a) and the power p k The smaller the influence of (c); 1a denotes the range of power consumed by task k,
represents the minimum and maximum power consumed to transmit task k; 1b indicates that the service vehicle j can serve at most eta j Task η j Load capacity for service vehicle j; 1c denotes a transmission data rate constraint, where k Is the minimum transmission data rate for task k.
Step 1.4: to ensure that the problem function and the constraint function are convex functions, the solution must be made
And p k The problem of coupling of the 2 variables is that a new variable is introduced for this purpose
And 2 new constraints (2a) and (2b) are introduced to guarantee
In any case can be equal to
And the function does not contain the multiplied part of the two,
all possible values are as follows:
additional constraints:
with this transformation, the P1 equivalent transformation becomes P2:
wherein
Comprises the following steps:
step 1.5: by x k Instead of the former
In the set of (1), the variables are i and j, k is the quantity, h denotes h ij In which i
And j is a variable. Then, a convex set (d.c.) plan is used to solve P2, and P2 is converted into P3:
P3:
wherein:
step 1.6: the data transmission rate is approximated by a linear function in a differential manner, and the method comprises the following steps:
the equation is converted into a log- (kx + b) form, so that the concave-convex property of the function is unique, and an approximate optimal solution can be obtained through iteration, so that P3 is converted into P4:
wherein l is the iteration number, because the solution of the convex difference can always approximate the optimal solution, the optimal solution point of the iterative approximation through the linear function tends to approachClose to the optimal solution point of the original function. Wherein
The value for the last iteration is a certain number. Initial feasible solution
Is prepared by reacting a compound of formula (I) with Q (x) k ) A convex optimization problem solved for the objective function. And when l reaches the preset maximum value, the iteration is terminated.
And 2, step: aiming at different computing requirements of different data, a vehicle generates a multi-task data stream and starts to send task unloading requirement information to a management node in a communication range, wherein the task unloading requirement information comprises a delay requirement, a data volume and a data type;
and 3, step 3: the vehicle operation region is divided into hexagonal grids, a base station closest to the center in each grid is used as a management node, and the management node obtains information of service vehicles in the range through broadcasting;
and 4, step 4: the management node refers to the model established in the step 1, and performs task unloading decision by integrating task information and service vehicle information, and the specific steps are as follows:
step 4 a): taking a client vehicle set, a service vehicle set and an unallocated task set as input;
step 4 b): setting a variable alpha according to the sensitivity of the application to the data rate, and setting parameters such as an iteration index to be 0, the maximum iteration times, convergence tolerance and the like;
step 4 c): the function P3 for maximizing the total task data transmission rate and the average power consumption in step 1 may be expressed as a resolvable function Q (x) k ) Minus a non-convex-non-concave function Z (x) k ) By applying a non-convex non-concave function Z (x) k ) Setting zero to obtain an initial distribution decision set x0 and x0 as a standard distribution decision set;
step 4 d): by applying a function Z (x) k ) Linearization is carried out to obtain Z (x) k ) The function for maximizing the total task data transmission rate and the average value of energy consumption is expressed as a convex value which can be solvedA function P4; step 4 e): substituting the standard distribution decision set into P4 to obtain a new distribution decision set, taking the new distribution set as the standard distribution decision set, adding 1 to the iteration index, and if the convergence is greater than the preset convergence tolerance or the iteration index is less than the preset maximum iteration times, recalculating according to the step 4 e);
and 5: according to the step 4, obtaining an optimal task allocation decision set, and sending the set to the client vehicle;
step 6: the client vehicle converts the task data stream into parallel signal vectors in a serial-parallel mode;
and 7: carrying out digital pre-coding on the parallel signal vectors, simulating pre-coding, generating transmission signal vectors and starting transmission through multiple antennas;
and 8: the transmission signal vector is transmitted to a service vehicle through a beam forming technology, and the service vehicle receives the specified task data through decoding and starts a calculation task.
And step 9: and transmitting the decision scheme obtained through calculation back to the client vehicle, and starting decision execution by the client vehicle, so that the efficient parallel unloading task is realized when the vehicle with multiple antennas cooperates with automatic driving, the data transmission rate is improved, and the energy consumption is reduced.
Has the advantages that:
1. compared with other task unloading methods, the multi-task allocation method applicable to cooperative automatic driving disclosed by the invention has the advantages that the channel capacity is improved by utilizing an MIMO system, the multi-antenna array is applied to vehicle-mounted fog calculation, the problem of concurrent calculation is solved by adopting a convex optimization method, and a plurality of task streams are received and transmitted simultaneously, so that the data transmission rate is effectively improved, and the vehicle can have higher response speed.
2. Compared with the similar task unloading method using multiple antennas, the multi-task allocation method suitable for cooperative automatic driving disclosed by the invention realizes a more reasonable allocation scheme through a hybrid pre-coding and convex optimization method, so that the data transmission rate is higher, the energy consumption is greatly reduced, and the vehicle has longer endurance.
Drawings
FIG. 1 is a schematic flow chart diagram of a multitask allocation method suitable for collaborative autonomous driving according to the present disclosure;
fig. 2 is a comparison graph of transmission rates of a multitask allocation method and a reference task allocation method with different rate sensitivities α in a collaborative autonomous driving scenario.
FIG. 3 is a graph comparing energy consumption of a multi-task allocation method with different rate sensitivities α and a reference task allocation method in a collaborative autonomous driving scenario.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples. While the technical problems and advantages of the present invention have been described in detail, it should be noted that the embodiments described are only intended to facilitate the understanding of the present invention, and should not be construed as limiting.
First, a MIMO system will be described. A MIMO system (Multiple-Input Multiple-Output) is a system in which a plurality of transmitting antennas and receiving antennas are used at a transmitting end and a receiving end, respectively, so that signals are transmitted and received through the plurality of antennas at the transmitting end and the receiving end, thereby improving communication quality. The multi-antenna multi-transmission multi-receiving system can fully utilize space resources, realize multi-transmission and multi-reception through a plurality of antennas, and improve the system channel capacity by times under the condition of not increasing frequency spectrum resources and antenna transmitting power.
In the application scenario of cooperative automatic driving, in order to improve the frequency utilization rate and increase the system capacity, a frequency reuse technology is often adopted, which can cause the problem of co-channel interference. The same frequency interference means that the carrier frequency of the useless signal is the same as that of the useful signal, and the interference is caused to a receiver receiving the same frequency useful signal. The cooperative automatic driving application, such as the application of APA automatic parking assistance, AVM panoramic monitoring, TJP traffic congestion navigation, etc., often generates a great amount of calculation requirements, and also needs to transmit and process a great amount of data, and because the application scene is sensitive to time and needs to reduce transmission time as much as possible, how to improve the data transmission rate of task unloading of the cooperative automatic driving application by using the MIMO system makes the vehicle response more sensitive, reduces energy consumption and increases vehicle endurance, and meanwhile, can reduce the problems of co-channel interference and noise caused by the MIMO system, which is the problem that the present invention aims to solve.
The embodiment is a specific application of the multi-task allocation method suitable for collaborative automatic driving in an automatic driving scene. In the embodiment, the real tracks of the buses appearing in a Helsinki 1 square kilometer area during 2018, 9, 18, 08: 00-08: 05 are collected, the latitude range of the area is 60 degrees 1201500-60 degrees 1205000, the longitude range is 24 degrees 5301500-24 degrees 5402400, in addition, 240 vehicle tracks in the period are generated by utilizing SUMO simulation software, wherein 50% of the vehicles are considered to have the cooperative automatic driving calculation requirement and need to be subjected to task unloading. The specific steps of task unloading are as follows:
step 1: modeling a non-convex and non-concave optimization problem of the task allocation scheme into a solvable approximate optimization problem, and realizing effective reduction of energy consumption and delay by solving the approximate optimal task allocation scheme;
step 1.1: a plurality of task data streams generated by a single client vehicle are converted into parallel signal vectors s through serial-to-parallel conversion i From
That is, parallel signal vectors pass through digital pre-coding matrix and analog pre-coding matrix to obtain transmission vector x i Where I ═ { I } denotes a client vehicle, J ═ { J } denotes a set of server vehicles, K i Where denotes a task generated by the customer vehicle i, a ij 0,1 represents the geographic association of the client vehicle i and the service vehicle j, with a value of 1 if j is within communication range of i,
indicating whether task k will be assigned to service vehicle j, if the value is 1, then it is assigned, if it is 0, G i A digital precoding matrix for vehicle i, B i For the simulated precoding matrix, s, of vehicle i i ＝[s1,s2,···,s|K i |]Representing parallel signal vectors, s, generated by vehicle i k Signal representing task k, g k A digital precoding vector, x, representing task k i Indicating vehiclesi's transmission signal vector;
step 1.2: transmission vector x i The signal vector received by the service vehicle can be obtained through channel matrix transformation
Decoding to obtain signal vector
The system consists of three parts, namely an expected signal of a service vehicle, other interference signals and noise interference. According to
The antenna array channel capacity for task k between customer vehicle i and service vehicle j can be calculated as
Wherein sigma 2 Is the variance of white Gaussian noise and is a constant; k ' of strip refers to removing the set K ' of K after the ith task ' i The task of (1). Setting the use of a uniform digital precoding method for each task generated, i.e. g k G, let h ij ＝|f j ·H ij ·g·B i L2, then simplify
Is composed of
Indicating the transmission power allocated to task k, f j Is the analog beamforming vector used by the service vehicle j, H ij Representing channel moments between client i and server jThe number of the arrays is determined,
an antenna array channel capacity representing a task k between a customer vehicle i and a service vehicle j;
step 1.3: setting a sensitivity variable α representing the data rate, the optimization problem function P1 is
Wherein alpha is [0,1 ]]The weight indicating the degree of sensitivity to the rate, it is clear that the larger the α, the larger the
The greater the influence of (a) and the power p k The smaller the influence of (c); 1a denotes the range of power consumed by task k,
represents the minimum and maximum power consumed to transmit task k; 1b indicates that the service vehicle j can serve at most eta j Task η j Load capacity for service vehicle j; 1c denotes a transmission data rate constraint, where k Is the minimum transmission data rate for task k.
Step 1.4: to ensure that the problem function and the constraint function are convex functions, the solution must be made
And p k The problem of coupling of the 2 variables is that a new variable is introduced for this purpose
And 2 new constraints (2a) and (2b) are introduced to guarantee
At any rateCan be equal to
And the function does not contain the multiplied part of the two,
all possible values are as follows:
additional constraints:
with this transformation, the P1 equivalent transformation becomes P2:
wherein
Comprises the following steps:
step 1.5: by x k Instead of the former
A variable ofi and j, k are quantitative and h is ij In which i
And j is a variable. Then, the solution P2 was solved using convex set (d.c.) planning, P2 was converted to P3:
P3:
wherein:
step 1.6: the data transmission rate is approximated by a linear function in a differential manner, and the method comprises the following steps:
the equation is converted into a log- (kx + b) form, so that the concave-convex property of the function is unique, and an approximate optimal solution can be obtained through iteration, so that P3 is converted into P4:
wherein l is the iteration number, because the convex difference solution can always approximate the optimal solution, the optimal solution point of the linear function iterative approximation approaches to the optimal solution point of the original function. Wherein
The value for the last iteration is a certain number. Initial feasible solution
Is prepared by reacting a compound of formula (I) with Q (x) k ) A convex optimization problem solved for the objective function. And when l reaches the preset maximum value, the iteration is terminated.
Step 2: through calculations, the 15 buses and 120 cars present in the simulation become the service vehicle and the client vehicle, respectively, assuming that each client vehicle will generate 3 tasks for the computational requirements of the application. Starting to send task unloading demand information to the management nodes in the communication range;
the driver selects a certain cooperative automatic driving function, such as city navigation, a client vehicle starts to acquire data through a camera, an ultrasonic radar, a millimeter wave radar and other sensors, vehicle state information is generated in real time, and meanwhile, the driver may start an entertainment function to generate related data.
And step 3: in order to secure the transmission data rate, the effective communication distance of the service vehicle is set to 100 meters. In addition, the management node obtains information of the service vehicles within the range by broadcasting, including position and load information, and in the simulation, the load capacity η of the service vehicles j Values of (d) vary from 18 to 69 to ensure that the iterative problem is solvable;
and 4, step 4: the management node refers to the model established in the step 1, and performs task unloading decision by integrating task information and service vehicle information, and the specific steps are as follows:
step 4 a): taking the client vehicle set i as 120, the service vehicle set j as 5 and the unallocated task set as input;
step 4 b): setting the power range consumed by the launch task to [10dBm,23dBm](i.e., [0.01W,0.2W ]]) And 0.1W is used as the initial transmission power. The antenna array channel parameter values, h, of all associated customer and service vehicle pairs can be obtained by multiple iterations ij 2046. In the simulation, assuming that the antenna array of each vehicle is omnidirectional, the variable σ is set to 0.1, the iteration index is set to 0, and the maximum number of iterations is set to 30. The scalar weight a refers to the trade-off between transmission data rate and power consumption. Alpha (alpha) ("alpha")The smaller, the more sensitive the proposed strategy is to power and vice versa. Adjusting α from 0.2 to 1 in steps of 0.2;
step 4 c): the function P3 for maximizing the total task data transmission rate and the average power consumption in step 1 may be expressed as a resolvable function Q (x) k ) Minus a non-convex-non-concave function Z (x) k ) By fitting a non-convex non-concave function Z (x) k ) Setting zero to obtain an initial distribution decision set x0 and x0 as a standard distribution decision set;
step 4 d): by pairing functions Z (x) k ) Linearization is carried out to obtain Z (x) k ) The function for maximizing the total task data transmission rate and the average value of energy consumption is expressed as a convex function P4 which can be solved; step 4 e): substituting the standard distribution decision set into P4 to obtain a new distribution decision set, taking the new distribution set as the standard distribution decision set, adding 1 to the iteration index, and if the convergence is greater than the preset convergence tolerance or the iteration index is less than the preset maximum iteration times, recalculating according to the step 4 e);
and 5: according to the step 4, obtaining an optimal task allocation decision set, and sending the set to the client vehicle;
and 6: the client vehicle converts the task data stream into parallel signal vectors in a serial-parallel mode;
and 7: carrying out digital pre-coding on the parallel signal vectors, simulating pre-coding, generating transmission signal vectors and starting transmission through multiple antennas;
and 8: the transmission signal vector is transmitted to a service vehicle through a beam forming technology, and the service vehicle receives the specified task data through decoding and starts a calculation task.
And step 9: the calculated decision scheme is transmitted back to the customer vehicle, which begins decision execution.
For comparison, the simulation implemented a sophisticated task allocation method being applied, called the benchmark task allocation method, in which the task allocation operation was load balanced with the running service vehicles using a probability distribution method. In the reference task allocation method, the more tasks currently carried by the service vehicle, the lower the probability that a new task is allocated to the service vehicle. Specifically, the probability of assigning a task to a service vehicle is:
fig. 2 is a comparison graph of transmission rates of a task allocation method with different rate sensitivities α and a reference task allocation method in a collaborative autonomous driving scenario. The abscissa is the reference task allocation method, the inventive method with α of 0.4,0.6 and 0.8, and the ordinate is the comparison with the transmission power of the reference task allocation method. The average transmission data rate increases with an increase in a. When α is 1, the maximum data rate can reach 2.07bps/Hz, and α is 0.4,0.6, or 0.8, which are respectively 5.8%, 6.4%, or 8.2% higher than the reference method in terms of the transmission data rate.
FIG. 3 is a comparison graph of energy consumption of a task allocation method with different rate sensitivities α and a reference task allocation method in a collaborative autonomous driving scenario. The abscissa is the reference task allocation method, the inventive method with α of 0.4,0.6 and 0.8, and the ordinate is the comparison with the energy consumption of the reference task allocation method. The average energy consumption increases with increasing alpha. When alpha is 0.2, the minimum power consumption can reach 0.03W. As α changes from 0.8 to 1, the average energy consumption increases significantly. This is because when α is 1, the average power consumption can almost reach its upper limit. When α is 0.4 and α is 0.8, the energy consumption is reduced by 38.3% and 23.3%, respectively, compared with the reference method.
Referring to fig. 2, fig. 3 shows that the highest transmission data rate is obtained when α is 0.8, and the lowest power consumption is obtained when α is 0.4. Therefore, when the application task is particularly sensitive to the delay, we recommend setting α to 0.8, and when the application task is not sensitive to the delay, we recommend setting α to 0.4, which can effectively save energy.
The invention is suitable for a multi-task allocation method based on convex optimization and cooperated with automatic driving vehicle fog calculation, effectively utilizes vehicle-mounted multiple antennas in a vehicle fog calculation network to carry out parallel task unloading, and can improve the data transmission rate to the maximum extent and reduce the energy consumption.
The above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (3)
1. A multitask allocation method suitable for vehicle fog calculation in collaborative automatic driving is characterized in that: comprises the following steps of (a) carrying out,
step 1: modeling a non-convex and non-concave optimization problem of the task allocation scheme into a solvable approximate optimization problem, and realizing effective reduction of energy consumption and delay by solving the approximate optimal task allocation scheme;
step 2: aiming at different calculation requirements of different data, the vehicle generates a multitask data stream, and starts to send task unloading requirement information to a management node in a communication range, wherein the task unloading requirement information comprises a delay requirement, a data volume and a data type;
and 3, step 3: the vehicle operation region is divided into hexagonal grids, a base station closest to the center in each grid is used as a management node, and the management node obtains information of service vehicles in the range through broadcasting;
and 4, step 4: the management node refers to the model established in the step 1, and task unloading decision is carried out through comprehensive task information and service vehicle information;
and 5: according to step 4, solving an optimal task allocation decision set, and sending the set to the client vehicle;
step 6: the client vehicle converts the task data stream into parallel signal vectors in a serial-parallel mode;
and 7: carrying out digital pre-coding on the parallel signal vectors, simulating pre-coding, generating transmission signal vectors and starting transmission through multiple antennas;
and 8: the transmission signal vector is transmitted to a service vehicle through a beam forming technology, and the service vehicle receives the specified task data through decoding and starts a calculation task;
and step 9: and the decision scheme obtained through calculation is transmitted back to the client vehicle, and the client vehicle starts decision execution, so that the efficient parallel unloading task is realized when the vehicle with multiple antennas is cooperatively and automatically driven, the data transmission rate is improved, and the energy consumption is reduced.
2. A multitask allocation method adapted for coordinated autonomous driving according to claim 1, characterized in that: the implementation method of the step 1 is that,
step 1.1, a plurality of task data streams generated by a single customer vehicle are converted into parallel signal vectors s through serial-to-parallel conversion i From
That is, the parallel signal vector obtains the transmission vector x through the digital pre-coding matrix and the analog pre-coding matrix i Where I ═ { I } denotes a client vehicle, J ═ { J } denotes a server vehicle set, K i K denotes a task generated by the customer vehicle i, a ij Where 0,1 represents the geographic association of the client vehicle i and the service vehicle j, with a value of 1 if j is within communication range of i,
indicating whether task k will be assigned to service vehicle j, is assigned when the value is 1, is not assigned when the value is 0, G i A digital precoding matrix for vehicle i, B i For the simulated precoding matrix, s, of vehicle i i ＝[s1，s2，···，s|K i |]Representing parallel signal vectors, s, generated by vehicle i k Signal representing task k, g k A digital precoding vector, x, representing task k i A transmission signal vector representing vehicle i;
step 1.2: transmission vector x i The signal vector received by the service vehicle can be obtained through channel matrix transformation
Decoding to obtain signal vector
The system consists of three parts, namely an expected signal of a service vehicle, other interference signals and noise interference; according to
The antenna array channel capacity for task k between customer vehicle i and service vehicle j can be calculated as
Wherein σ 2 Is the variance of white Gaussian noise and is a constant; k ' of strip refers to removing the set K ' of K after the ith task ' i The task of (1); setting the use of a uniform digital precoding method for each task generated, i.e. g k G, let h ij ＝|f j ·H ij ·g·B i L2, then simplify
Is composed of
Indicating the transmission power allocated to task k, f j Is the analog beamforming vector used by the service vehicle j, H ij Representing the channel matrix between client i and server j,
an antenna array channel capacity representing a task k between a customer vehicle i and a service vehicle j;
step 1.3: setting a sensitivity variable d representing the data rate, the optimization problem function P1 is
s.t.
Wherein alpha is [0,1 ]]The weight indicating the degree of sensitivity to the rate, it is clear that the larger the α, the larger the
The greater the influence of (a) and the power p k The smaller the influence of (c); 1a denotes the range of power consumed by task k,
ξrepresents the minimum and maximum power consumed to transmit task k; 1b indicates that the service vehicle j can serve at most eta j Task η j Load capacity for service vehicle j; 1c denotes a transmission data rate constraint, where k A minimum transmission data rate for task k;
step 1.4: to ensure that the problem function and the constraint function are convex functions, the solution must be made
And p k The problem of coupling of the 2 variables is that a new variable is introduced for this purpose
And 2 new constraints (2a) and (2b) are introduced to guarantee
In any case can be equal to
And the function does not contain the portion of the multiplication of the two,
all possible values are as follows:
additional constraints:
with this transformation, the P1 equivalent transformation becomes P2:
wherein
Comprises the following steps:
step 1.5: by x k Instead of the former
With the variables i and j, k being quantitative and h being ij Wherein i and j are variables; then, the solution P2 was solved using convex set (d.c.) planning, P2 was converted to P3:
wherein:
step 1.6: the data transmission rate is approximated by a linear function in a differential manner, and the method comprises the following steps:
the equation is converted into a log- (kx + b) form, so that the concave-convex property of the function is unique, and an approximate optimal solution can be obtained through iteration, so that P3 is converted into P4:
wherein 1 is iteration times, because the convex difference solution can always approximate the optimal solution, the optimal solution point approximated by linear function iteration can approach the optimal solution point of the original function; wherein
The value for the last iteration is a determined number; initial feasible solution
Is prepared by reacting a compound of formula (I) with Q (X) k ) A convex optimization problem solved for the objective function; the iteration terminates when 1 reaches a preset maximum.
3. A multitask allocation method adapted for coordinated autonomous driving according to claim 1, characterized in that: the implementation method of the step 4 is that,
step 4 a): taking a client vehicle set, a service vehicle set and an unallocated task set as input;
step 4 b): setting a variable alpha according to the sensitivity of the application to the data rate, and setting parameters such as an iteration index to be 0, the maximum iteration times, convergence tolerance and the like;
step 4 c): the function P3 for maximizing the total task data transmission rate and the average power consumption in step 1 may be expressed as a resolvable function Q (x) k ) Minus a non-convex-non-concave function Z (x) k ) By fitting a non-convex non-concave function Z (x) k ) Setting zero to obtain an initial distribution decision set x0 and x0 as a standard distribution decision set;
step 4 d): by applying a function Z (x) k ) Linearization is carried out to obtain Z (x) k ) The function that maximizes the total task data transmission rate and the mean value of energy consumption is represented as a convex function P4 that can be solved;
step 4 e): substituting the standard distribution decision set into P4 to obtain a new distribution decision set, taking the new distribution set as the standard distribution decision set, adding 1 to the iteration index, and if the convergence is greater than the predetermined convergence tolerance or the iteration index is less than the predetermined maximum iteration number, recalculating according to step 4 e).
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